Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/16686#discussion_r97690665
--- Diff:
external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaRelationSuite.scala
---
@@ -0,0 +1,215 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.kafka010
+
+import java.util.concurrent.atomic.AtomicInteger
+
+import org.apache.kafka.common.TopicPartition
+import org.scalatest.BeforeAndAfter
+
+import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.test.{SharedSQLContext, TestSparkSession}
+
+class KafkaRelationSuite extends QueryTest with BeforeAndAfter with
SharedSQLContext {
+
+ import testImplicits._
+
+ private val topicId = new AtomicInteger(0)
+
+ private var testUtils: KafkaTestUtils = _
+
+ private def newTopic(): String = s"topic-${topicId.getAndIncrement()}"
+
+ private def assignString(topic: String, partitions: Iterable[Int]):
String = {
+ JsonUtils.partitions(partitions.map(p => new TopicPartition(topic, p)))
+ }
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ testUtils = new KafkaTestUtils
+ testUtils.setup()
+ }
+
+ override def afterAll(): Unit = {
+ if (testUtils != null) {
+ testUtils.teardown()
+ testUtils = null
+ super.afterAll()
+ }
+ }
+
+ test("explicit earliest to latest offsets") {
+ val topic = newTopic()
+ testUtils.createTopic(topic, partitions = 3)
+ testUtils.sendMessages(topic, (0 to 9).map(_.toString).toArray,
Some(0))
+ testUtils.sendMessages(topic, (10 to 19).map(_.toString).toArray,
Some(1))
+ testUtils.sendMessages(topic, Array("20"), Some(2))
+
+ // Specify explicit earliest and latest offset values
+ val reader = spark
+ .read
+ .format("kafka")
+ .option("kafka.bootstrap.servers", testUtils.brokerAddress)
+ .option("subscribe", topic)
+ .option("startingOffsets", "earliest")
+ .option("endingOffsets", "latest")
+ .load()
+ var df = reader.selectExpr("CAST(value AS STRING)")
+ checkAnswer(df, (0 to 20).map(_.toString).toDF)
+
+ // "latest" should late bind to the current (latest) offset in the
reader
+ testUtils.sendMessages(topic, (21 to 29).map(_.toString).toArray,
Some(2))
+ df = reader.selectExpr("CAST(value AS STRING)")
+ checkAnswer(df, (0 to 29).map(_.toString).toDF)
+ }
+
+ test("default starting and ending offsets") {
+ val topic = newTopic()
+ testUtils.createTopic(topic, partitions = 3)
+ testUtils.sendMessages(topic, (0 to 9).map(_.toString).toArray,
Some(0))
+ testUtils.sendMessages(topic, (10 to 19).map(_.toString).toArray,
Some(1))
+ testUtils.sendMessages(topic, Array("20"), Some(2))
+
+ // Implicit offset values, should default to earliest and latest
+ val df = spark
+ .read
+ .format("kafka")
+ .option("kafka.bootstrap.servers", testUtils.brokerAddress)
+ .option("subscribe", topic)
+ .load()
+ .selectExpr("CAST(value AS STRING)")
+ // Test that we default to "earliest" and "latest"
+ checkAnswer(df, (0 to 20).map(_.toString).toDF)
+ }
+
+ test("explicit offsets") {
+ val topic = newTopic()
+ testUtils.createTopic(topic, partitions = 3)
+ testUtils.sendMessages(topic, (0 to 9).map(_.toString).toArray,
Some(0))
+ testUtils.sendMessages(topic, (10 to 19).map(_.toString).toArray,
Some(1))
+ testUtils.sendMessages(topic, Array("20"), Some(2))
+
+ // Test explicitly specified offsets
+ val startPartitionOffsets = Map(
+ new TopicPartition(topic, 0) -> -2L, // -2 => earliest
+ new TopicPartition(topic, 1) -> -2L,
+ new TopicPartition(topic, 2) -> 0L // explicit earliest
+ )
+ val startingOffsets = JsonUtils.partitionOffsets(startPartitionOffsets)
+
+ val endPartitionOffsets = Map(
+ new TopicPartition(topic, 0) -> -1L, // -1 => latest
+ new TopicPartition(topic, 1) -> -1L,
+ new TopicPartition(topic, 2) -> 1L // explicit offset happens to =
the latest
+ )
+ val endingOffsets = JsonUtils.partitionOffsets(endPartitionOffsets)
+ val reader = spark
+ .read
+ .format("kafka")
+ .option("kafka.bootstrap.servers", testUtils.brokerAddress)
+ .option("subscribe", topic)
+ .option("startingOffsets", startingOffsets)
+ .option("endingOffsets", endingOffsets)
+ .load()
+ var df = reader.selectExpr("CAST(value AS STRING)")
+ checkAnswer(df, (0 to 20).map(_.toString).toDF)
+
+ // static offset partition 2, nothing should change
+ testUtils.sendMessages(topic, (31 to 39).map(_.toString).toArray,
Some(2))
+ df = reader.selectExpr("CAST(value AS STRING)")
+ checkAnswer(df, (0 to 20).map(_.toString).toDF)
+
+ // latest offset partition 1, should change
+ testUtils.sendMessages(topic, (21 to 30).map(_.toString).toArray,
Some(1))
+ df = reader.selectExpr("CAST(value AS STRING)")
+ checkAnswer(df, (0 to 30).map(_.toString).toDF)
+ }
+
+ test("reuse same dataframe in query") {
+ // This test ensures that we do not cache the Kafka Consumer in
KafkaRelation
+ val topic = newTopic()
+ testUtils.createTopic(topic, partitions = 1)
+ testUtils.sendMessages(topic, (0 to 10).map(_.toString).toArray,
Some(0))
+
+ // Ensure local[2] so that two tasks will execute the query on one
partition
+ val testSession = new TestSparkSession(sparkContext)
+ // Specify explicit earliest and latest offset values
+ val reader = testSession
+ .read
+ .format("kafka")
+ .option("kafka.bootstrap.servers", testUtils.brokerAddress)
+ .option("subscribe", topic)
+ .option("startingOffsets", "earliest")
+ .option("endingOffsets", "latest")
+ .load()
+ var df = reader.selectExpr("CAST(value AS STRING)")
+ checkAnswer(df.union(df),
+ (0 to 10).map(_.toString).toDF.union((0 to 10).map(_.toString).toDF))
--- End diff --
nit: `((0 to 10) ++ (0 to 10)).map(_.toString).toDF`.
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